Convert CSV Latitude longtitude to Blender coordinates
Here's how you read the CSV, reformat the dates, sort the quakes by date, convert lat/long to x/y/z then output each row as a text object on the outside of a hypothetical sphere (with the object name set to the date of the quake in YYYY-MM-DD format) with the text oriented so that it sits flat on the surface. Fonts and materials and other tweaks to be supplied by you. :)

Based this off https://stackoverflow.com/questions/10473852/convert-latitude-and-longitude-to-point-in-3d-space

# imports to make all the things work
from math import sin, cos, radians, pi, atan, tan
import csv
import bpy

# define some helpful functions for later

# a function to make, scale and rotate text objects into place
def makeathing(coords, scale, lat, long, date):
    rotation = (radians(lat - 270), pi, radians(long - 90))
    bpy.ops.object.text_add(location=(coords), rotation=(rotation))
    ob = bpy.context.object
    # set object properties here
    ob.data.body = "{:.1f}".format(float(scale))
    ob.data.align = 'CENTER'
    ob.rotation_mode = 'YXZ'
    ob.data.size = 0.03
    ob.data.extrude = 0.002
    ob.data.name = date
    ob.name = date

# a function to turn 3D polar coordinates into 3D cartesian coordinates
def latlonger(lat_deg, lon_deg):
    lat = radians(lat_deg)
    lon = radians(lon_deg)
    # see: http://www.mathworks.de/help/toolbox/aeroblks/llatoecefposition.html
    alt = 0.01
    rad = 2                                     # radius of sphere in BUs
    f  = 0.0                                    # flattening
    ls = atan((1 - f)**2 * tan(lat))  # lambda
    x = rad * cos(lat) * cos(lon) + alt * cos(lat) * cos(lon)
    y = rad * cos(lat) * sin(lon) + alt * cos(lat) * sin(lon)
    z = rad * sin(lat) + alt * sin(lat)
    return (x, y, z)

# NB: no code has executed yet, we're just setting up functions we use further down

# read in the CSV file containing quake data
file = csv.reader(open('\\Users\mcsweend2c\Desktop\Bearthquakes 6-9.csv', newline=''), delimiter=',')

# make a blank dict variable to put quake data into
quakes = {}

# iterate through the CSV row by row and pull the data into variables
# then put the variables into a tuple and use a corrected date as the dict key
# see the last line of the for block for data order
# NB: could have used a dict instead of a tuple
# would have meant looking up e.g. quakes[x]['scale'] instead of quakes[x][0]
for idx, (scale, date, lat, lon) in enumerate(file):
    (yd, m, dy) = date.split("/")
    if int(yd) &gt; int(dy):
        year = yd
        day = dy
    else:
        year = dy
        day = yd
    key = "{}-{:02g}-{:02g}".format( year, int(m), int(day) )
    quakes[key] = (scale, lat, lon)

# go through the sorted dates in the quakes dict
# get latitude and longitude and turn into x/y/z coords with latlonger function
# then send x/y/z coordinates to makeathing function to create them in 3D space
for k in sorted(quakes.keys()):
    latitude = float(quakes[k][1])
    longitude = float(quakes[k][2])
    coords = latlonger(latitude, longitude)
    makeathing(coords, quakes[k][0], latitude, longitude, k)


You may need to offset the X of the world map texture to align the letters to their correct positions on the globe, e.g. using this texture map I had to offset X by 0.25.